Hi HN, we're releasing weights for our latest text to image model and publishing this writeup on how we trained it in quite a bit of depth.
I hope there is something in the report for everyone, we included a fair bit on the actual training and data infrastructure usually not written about much, that I think will be interesting to people here. There's more that didn't fit, happy to answer questions!
Neat! Between Ideogram4, Flux2, Qwen-Image, ZiT, and Krea - there's been a lot of positive movement in the open-weights space.
The original Flux.1 Krea is actually in my GenAI Showdown benchmark site from all the way back in July of last year (which feels like a lifetime in this space), so I’m looking forward to putting this new one through its paces.
This is a massive technical report for an open weights image gen model. As someone who has followed this space closely, it’s really cool to read about the behind-the-scenes experimentation and effort that went into the final product. I hope you will release some of the find tuning tools so the community can experiment with them as well and really push what the model’s capable of.
You can find some links and details in the GitHub readme for finetuning / LoRA support. Ostiris, musubi tuner, fal and hugging face diffusers are all day-0 supported :)
https://github.com/krea-ai/krea-2
We recommend training off the undistilled, Raw checkpoint, and then applying the LoRA to the Turbo model for inference.
It's pretty great that you are providing the undistilled model on day 0. Here's a pro-tip: With Flux.2 Klein, someone created a turbo slider LoRA - basically a diff of the turbo 9B model vs. the undistilled 9B model. What's great about this LoRA is that you can sample using a heavier weighting of the undistilled weights during early sampling steps and then finish the sampling off with mostly the distilled weights. The result is a better "finish" (taking advantage of the distilled model's refinement for image quality) without sacrificing the undistilled model's greater ability to adhere to the prompt, because the undistilled model doesn't have to devote its weights so much to looking good.
What is Krea's approach to content such as pornography and gore? It's been frustrating to see all of the leading models take a very hard line on excluding vice content, even when it is perfectly legal, in the name of safety.
We are releasing the weights and a _juicy_ technical report---at least given current industry standards. In it we describe data curation/captioning, model architecture, post-training, RL pipelines, prompt expansion, style references, and our infrastructure in great detail.
When it comes to theweights themselves, there's actually 2 releases:
* Krea 2 Turbo. This model is both guidance- and timestep- distilled for faster inference.
* Krea 2 RAW. This model is actually meant to be hackable/fine-tunable
One of the things we think the (open) LLM community does well is release models in different sizes and also at different stages of the training pipelines; we are releasing two checkpoints at both the mid-training and post-training stage. This is rare in the image & multimedia community, so we can't help it but to feel proud of this release.
Good to have more open weight models, and I really appreciate the in-depth write-up.
I also like the "keep the manifold wide" approach of trying to make a model capable of many styles as opposed to getting it "dialed in" for a dozen of style presets.
But it does feel very much like "fighting the past war" - now that advanced "image-to-image"/"agentic composition" models like Nano Banana 2 or Images 2.0 are out there in force.
I seriously doubt that the basic Qwen 3 VL in cross can get anywhere near that level of I2I. And robust I2I is very desirable - editing, adjustment, character consistency, the generalization of whatever you're doing with style transfer now (underexplained BTW).
Trying to hit that level of I2I is not by any means easy, but it's pretty clear to me that this is where the next frontier for image models lies. Feels like Ideogram might be building up to it, but I'm yet to see it anywhere else in open weight space.
I appreciate the skepticism but we find internally that this model is used more than Nano Banana for many cases like moodboarding (also, 4x cheaper than NBP never hurts). Agentic workflows are compatible with Krea 2 so I’m not sure I follow there. If you are talking about an edit model, that’s coming too.
Also, we are on par with them in t2i benchmarks, check the artificial analysis link I posted in my top comment.
And you cannot re-train nano banana or ChatGPT to understand your brand, which is what our customers complain about constantly.
Plus open-source! It’s hard to do an apple to apple comparison.
"Compatible" is one thing - "built for" is a different beast. The difference can be like that between Images 1.0 and Images 2.0 - the sheer leap in compositional capabilities was staggering.
"Edit model" is a part of it, yes. So is style transfer. But less as an endpoint and more of a subset of what advanced I2I enables.
"Re-train to understand your brand" is a fine marketing pitch, but in practical terms, it's hard to justify burning a LoRA for most uses. Enthusiasts absolutely do it, but enthusiasts are built different. Robust I2I can accomplish a lot of the same, but with a workflow that's closer to "drag and drop your references" than to "try to get a LoRA to do what you wanted it to do on a very slim set of images".
Modern LoRA pipelines are getting closer to "reliable" and "braindead simple", but you can't escape the "wait N hours for the GPUs to churn" of fine tune no matter what you do. And iteration time kills - a lot of the value of AI in workflows is that it does what it does fast and allows you to iterate at speed.
You can think of "LoRA vs I2I" as of an image twin of "SFT vs in-context learning" of LLM land. Both are useful, neither substitutes for the other fully, but there's a reason why most reach for the latter way before they reach for the former.
I like the T2I from what I've seen, mind. Perhaps more than Images 2.0 or even NB2. I just think that focusing solely on T2I to the exclusion of advanced editing and composition capabilities is a very 2024 thing.
This model does image to image; whats the issue with Qwen 3 VL; is style transfer unexplained? " reference" is mentioned 11 times on the page (more specifically, I read it and it seemed to discuss it a lot)
A lot of coffee for sure. Regarding the training cost, it's hard to give a good estimate because we used a shared kubernetes cluster with inference + research workloads.
Krea 2 Large (on the website and api) was trained with the FLUX 2 VAE, if you want to test it out and push realism. After working with both I think the flux VAE has a slight edge in learning realistic textures but it's smaller than you might think, the Qwen VAE was overall very good in ablations and good at learning to produce a diverse set of styles.
Edit: your account has unfortunately been breaking the site guidelines like this in other places as well (e.g. https://news.ycombinator.com/item?id=48567675). Can you please fix this? I don't want to ban you, but we've already had to ask you this before.
Definitely encourage you to test the models. We tried to optimize for realistic focus and not over-sharpening, which leads to a "hyper" AI-look.
It's hard to benchmark because people generally prefer sharp, saturated orangish pictures all else equal, but I believe these are bad shortcuts for the model to learn realism.
Is my taste the problem, or am I simply holding it wrong? The qwen VAE's shortcomings are well documented, and Krea 2 produces the same blurry, airbrushed output as qwen image. Between the chaotic release and every interaction I've had with your team, I've grown to genuinely dislike your platform/company. Good luck.
There is a lot of discourse about it on Reddit. Check the AMA link I put in the comment above for learning more. The basics is it wasn’t released when we started and we use it for internal models and hope to do further open source releases.
Hi HN, we're releasing weights for our latest text to image model and publishing this writeup on how we trained it in quite a bit of depth.
I hope there is something in the report for everyone, we included a fair bit on the actual training and data infrastructure usually not written about much, that I think will be interesting to people here. There's more that didn't fit, happy to answer questions!
Neat! Between Ideogram4, Flux2, Qwen-Image, ZiT, and Krea - there's been a lot of positive movement in the open-weights space.
The original Flux.1 Krea is actually in my GenAI Showdown benchmark site from all the way back in July of last year (which feels like a lifetime in this space), so I’m looking forward to putting this new one through its paces.
This is a massive technical report for an open weights image gen model. As someone who has followed this space closely, it’s really cool to read about the behind-the-scenes experimentation and effort that went into the final product. I hope you will release some of the find tuning tools so the community can experiment with them as well and really push what the model’s capable of.
Thanks! You should definitely check out the r/stablediffusion sub-reddit; people are going crazy over it!
We also had 0-day support from people like Ostris and ComfyUI from the open source community
You can find some links and details in the GitHub readme for finetuning / LoRA support. Ostiris, musubi tuner, fal and hugging face diffusers are all day-0 supported :) https://github.com/krea-ai/krea-2
We recommend training off the undistilled, Raw checkpoint, and then applying the LoRA to the Turbo model for inference.
It's pretty great that you are providing the undistilled model on day 0. Here's a pro-tip: With Flux.2 Klein, someone created a turbo slider LoRA - basically a diff of the turbo 9B model vs. the undistilled 9B model. What's great about this LoRA is that you can sample using a heavier weighting of the undistilled weights during early sampling steps and then finish the sampling off with mostly the distilled weights. The result is a better "finish" (taking advantage of the distilled model's refinement for image quality) without sacrificing the undistilled model's greater ability to adhere to the prompt, because the undistilled model doesn't have to devote its weights so much to looking good.
What is Krea's approach to content such as pornography and gore? It's been frustrating to see all of the leading models take a very hard line on excluding vice content, even when it is perfectly legal, in the name of safety.
Hello HN,
I am Diego Rodriguez, Co-founder & CTO at Krea.
We are releasing the weights and a _juicy_ technical report---at least given current industry standards. In it we describe data curation/captioning, model architecture, post-training, RL pipelines, prompt expansion, style references, and our infrastructure in great detail.
When it comes to theweights themselves, there's actually 2 releases:
* Krea 2 Turbo. This model is both guidance- and timestep- distilled for faster inference.
* Krea 2 RAW. This model is actually meant to be hackable/fine-tunable
One of the things we think the (open) LLM community does well is release models in different sizes and also at different stages of the training pipelines; we are releasing two checkpoints at both the mid-training and post-training stage. This is rare in the image & multimedia community, so we can't help it but to feel proud of this release.
We are on par with Nano Banana in terms of image quality as per Artificial Analysis text-to-image benchmarks (https://artificialanalysis.ai/image/leaderboard/text-to-imag...).
We also attached a permissive license for individuals and small businesses.
Useful links:
- Marketing page around the OSS release: https://www.krea.ai/krea-2-open-source
- Huggingface model: https://www.krea.ai/krea-2/huggingface
- GitHub repository: https://www.krea.ai/krea-2/github
- Reddit AMA: https://www.reddit.com/r/StableDiffusion/comments/1udnm0a/we...
- Technical report: https://www.krea.ai/blog/krea-2-technical-report Thank you and I hope you enjoy this release---happy hacking!
Some of our team members will be answering questions since we are at the front page for now (thank you HN!).
Happy hacking!
Did anyone generate a pelican riding a bicycle?
I tried two of the Krea 2 models in LM Studio, but loading the downloaded models errored out. (Maybe I'm doing it wrong, since it's an image model.)
Previously: https://news.ycombinator.com/item?id=47800562
Image models need software like ComfyUI, not LM Studio.
Good to have more open weight models, and I really appreciate the in-depth write-up.
I also like the "keep the manifold wide" approach of trying to make a model capable of many styles as opposed to getting it "dialed in" for a dozen of style presets.
But it does feel very much like "fighting the past war" - now that advanced "image-to-image"/"agentic composition" models like Nano Banana 2 or Images 2.0 are out there in force.
I seriously doubt that the basic Qwen 3 VL in cross can get anywhere near that level of I2I. And robust I2I is very desirable - editing, adjustment, character consistency, the generalization of whatever you're doing with style transfer now (underexplained BTW).
Trying to hit that level of I2I is not by any means easy, but it's pretty clear to me that this is where the next frontier for image models lies. Feels like Ideogram might be building up to it, but I'm yet to see it anywhere else in open weight space.
I appreciate the skepticism but we find internally that this model is used more than Nano Banana for many cases like moodboarding (also, 4x cheaper than NBP never hurts). Agentic workflows are compatible with Krea 2 so I’m not sure I follow there. If you are talking about an edit model, that’s coming too.
Also, we are on par with them in t2i benchmarks, check the artificial analysis link I posted in my top comment.
And you cannot re-train nano banana or ChatGPT to understand your brand, which is what our customers complain about constantly.
Plus open-source! It’s hard to do an apple to apple comparison.
"Compatible" is one thing - "built for" is a different beast. The difference can be like that between Images 1.0 and Images 2.0 - the sheer leap in compositional capabilities was staggering.
"Edit model" is a part of it, yes. So is style transfer. But less as an endpoint and more of a subset of what advanced I2I enables.
"Re-train to understand your brand" is a fine marketing pitch, but in practical terms, it's hard to justify burning a LoRA for most uses. Enthusiasts absolutely do it, but enthusiasts are built different. Robust I2I can accomplish a lot of the same, but with a workflow that's closer to "drag and drop your references" than to "try to get a LoRA to do what you wanted it to do on a very slim set of images".
Modern LoRA pipelines are getting closer to "reliable" and "braindead simple", but you can't escape the "wait N hours for the GPUs to churn" of fine tune no matter what you do. And iteration time kills - a lot of the value of AI in workflows is that it does what it does fast and allows you to iterate at speed.
You can think of "LoRA vs I2I" as of an image twin of "SFT vs in-context learning" of LLM land. Both are useful, neither substitutes for the other fully, but there's a reason why most reach for the latter way before they reach for the former.
I like the T2I from what I've seen, mind. Perhaps more than Images 2.0 or even NB2. I just think that focusing solely on T2I to the exclusion of advanced editing and composition capabilities is a very 2024 thing.
This model does image to image; whats the issue with Qwen 3 VL; is style transfer unexplained? " reference" is mentioned 11 times on the page (more specifically, I read it and it seemed to discuss it a lot)
The example images look great. Always good to have more diverse OSS models.
Interesting item on the careers page btw. For anyone that knows what older school Mellanox was about, it might be your kind of thing: https://jobs.ashbyhq.com/krea/ebe94024-eef6-4306-a019-10072a... :D
Turbo appears GGUF'd already: https://huggingface.co/Abiray/Krea-2-Turbo-GGUF
Looking forward to playing with Krea 2, I use Z-Image Turbo daily -- it has replaced my stock photo subscriptions, for realism and illustrations.
May I ask how much did the training cost you?
A lot of coffee for sure. Regarding the training cost, it's hard to give a good estimate because we used a shared kubernetes cluster with inference + research workloads.
It's a good model sadly the use of the qwen vae is a bit of a downer.
Krea 2 Large (on the website and api) was trained with the FLUX 2 VAE, if you want to test it out and push realism. After working with both I think the flux VAE has a slight edge in learning realistic textures but it's smaller than you might think, the Qwen VAE was overall very good in ablations and good at learning to produce a diverse set of styles.
[flagged]
> You can't be serious.
Please edit out swipes from your HN comments, as the guidelines request: https://news.ycombinator.com/newsguidelines.html.
Edit: your account has unfortunately been breaking the site guidelines like this in other places as well (e.g. https://news.ycombinator.com/item?id=48567675). Can you please fix this? I don't want to ban you, but we've already had to ask you this before.
Kill the account dang.
Definitely encourage you to test the models. We tried to optimize for realistic focus and not over-sharpening, which leads to a "hyper" AI-look. It's hard to benchmark because people generally prefer sharp, saturated orangish pictures all else equal, but I believe these are bad shortcuts for the model to learn realism.
Is my taste the problem, or am I simply holding it wrong? The qwen VAE's shortcomings are well documented, and Krea 2 produces the same blurry, airbrushed output as qwen image. Between the chaotic release and every interaction I've had with your team, I've grown to genuinely dislike your platform/company. Good luck.
It's been mentioned by some that using the wan2.1 vae instead solves this. I haven't personally had time to try yet.
There is a lot of discourse about it on Reddit. Check the AMA link I put in the comment above for learning more. The basics is it wasn’t released when we started and we use it for internal models and hope to do further open source releases.